We propose a novel method for adjusting luminance for multi-exposure image
fusion. For the adjustment, two novel scene segmentation approaches based on
luminance distribution are also proposed. Multi-exposure image fusion is a
method for producing images that are expected to be more informative and
perceptually appealing than any of the input ones, by directly fusing photos
taken with different exposures. However, existing fusion methods often produce
unclear fused images when input images do not have a sufficient number of
different exposure levels. In this paper, we point out that adjusting the
luminance of input images makes it possible to improve the quality of the final
fused images. This insight is the basis of the proposed method. The proposed
method enables us to produce high-quality images, even when undesirable inputs
are given. Visual comparison results show that the proposed method can produce
images that clearly represent a whole scene. In addition, multi-exposure image
fusion with the proposed method outperforms state-of-the-art fusion methods in
terms of MEF-SSIM, discrete entropy, tone mapped image quality index, and
statistical naturalness.

本文提出了一种新颖的多曝光图像融合的亮度调整方法，该方法基于亮度分布提出了两种新颖的场景分割方法。实验结果表明，该方法可以在给定不理想输入时生成高质量的图像，并且在多个评测指标上优于其他最先进的方法。